Fixed Broken link for word_embedding

Fixed Broken link for  word_embedding notebook

Author: mohantym

site/en/tutorials/keras/text_classification.ipynb

@@ -648,7 +648,7 @@
       "source": [
         "The layers are stacked sequentially to build the classifier:\n",
         "\n",
-        "1. The first layer is an `Embedding` layer. This layer takes the integer-encoded reviews and looks up an embedding vector for each word-index. These vectors are learned as the model trains. The vectors add a dimension to the output array. The resulting dimensions are: `(batch, sequence, embedding)`.  To learn more about embeddings, see the [word embedding tutorial](../text/word_embeddings.ipynb).\n",
+        "1. The first layer is an `Embedding` layer. This layer takes the integer-encoded reviews and looks up an embedding vector for each word-index. These vectors are learned as the model trains. The vectors add a dimension to the output array. The resulting dimensions are: `(batch, sequence, embedding)`.  To learn more about embeddings, see the [word embedding tutorial](https://github.com/tensorflow/text/blob/master/docs/guide/word_embeddings.ipynb).\n",
         "2. Next, a `GlobalAveragePooling1D` layer returns a fixed-length output vector for each example by averaging over the sequence dimension. This allows the model to handle input of variable length, in the simplest way possible.\n",
         "3. This fixed-length output vector is piped through a fully-connected (`Dense`) layer with 16 hidden units. \n",
         "4. The last layer is densely connected with a single output node."